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\title{Specific or Generic? Lessons from the Domination Game}

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\author{Taco S. Cohen, Tijmen Blankevoort, Robrecht Jurriaans}


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\begin{abstract}
The domination game is a challenging, multi-agent, partially observable, inherently non-Markovian problem with large, continuous state and action spaces.
We present an analysis of the game, and show how insight into its structure can be used to construct a compact yet informative representation of the state,
as well as a finite set of high-level actions that effectively discretize the action space.
We show that, together with a simple coordination algorithm, this representation can be used to create a policy that performs very well in a competitive setting.
Furthermore, we show that given this representation, a simple policy can be evolved using the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, and analyze its learning performance.
Interestingly, our approach outperforms other contestants in the 2012 Domionation Game Competition by a large margin, even with a simple rule-based policy.
In light of this, we argue that the key to truly generic solution methods lies in creating effective representations -- something that is very problem specific and is often left to the researcher.

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